{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "keras_05_mnist_batch_norm.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.5.3"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Lvo0t7XVIkWZ"
      },
      "source": [
        "### Parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "cCpkS9C_H7Tl",
        "colab": {}
      },
      "source": [
        "BATCH_SIZE = 64\n",
        "EPOCHS = 10\n",
        "\n",
        "training_images_file   = 'gs://mnist-public/train-images-idx3-ubyte'\n",
        "training_labels_file   = 'gs://mnist-public/train-labels-idx1-ubyte'\n",
        "validation_images_file = 'gs://mnist-public/t10k-images-idx3-ubyte'\n",
        "validation_labels_file = 'gs://mnist-public/t10k-labels-idx1-ubyte'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "qpiJj8ym0v0-"
      },
      "source": [
        "### Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "AoilhmYe1b5t",
        "outputId": "1c6f12f9-c269-41b7-f90b-ba600bf45e8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import os, re, math, json, shutil, pprint\n",
        "import PIL.Image, PIL.ImageFont, PIL.ImageDraw\n",
        "import IPython.display as display\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from matplotlib import pyplot as plt\n",
        "tf.enable_eager_execution()\n",
        "print(\"Tensorflow version \" + tf.__version__)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensorflow version 1.14.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "colab_type": "code",
        "id": "qhdz68Xm3Z4Z",
        "colab": {}
      },
      "source": [
        "#@title visualization utilities [RUN ME]\n",
        "\"\"\"\n",
        "This cell contains helper functions used for visualization\n",
        "and downloads only. You can skip reading it. There is very\n",
        "little useful Keras/Tensorflow code here.\n",
        "\"\"\"\n",
        "\n",
        "# Matplotlib config\n",
        "plt.ioff()\n",
        "plt.rc('image', cmap='gray_r')\n",
        "plt.rc('grid', linewidth=1)\n",
        "plt.rc('xtick', top=False, bottom=False, labelsize='large')\n",
        "plt.rc('ytick', left=False, right=False, labelsize='large')\n",
        "plt.rc('axes', facecolor='F8F8F8', titlesize=\"large\", edgecolor='white')\n",
        "plt.rc('text', color='a8151a')\n",
        "plt.rc('figure', facecolor='F0F0F0', figsize=(16,9))\n",
        "# Matplotlib fonts\n",
        "MATPLOTLIB_FONT_DIR = os.path.join(os.path.dirname(plt.__file__), \"mpl-data/fonts/ttf\")\n",
        "\n",
        "# pull a batch from the datasets. This code is not very nice, it gets much better in eager mode (TODO)\n",
        "def dataset_to_numpy_util(training_dataset, validation_dataset, N):\n",
        "  \n",
        "  # get one batch from each: 10000 validation digits, N training digits\n",
        "  batch_train_ds = training_dataset.apply(tf.data.experimental.unbatch()).batch(N)\n",
        "  \n",
        "  # eager execution: loop through datasets normally\n",
        "  if tf.executing_eagerly():\n",
        "    for validation_digits, validation_labels in validation_dataset:\n",
        "      validation_digits = validation_digits.numpy()\n",
        "      validation_labels = validation_labels.numpy()\n",
        "      break\n",
        "    for training_digits, training_labels in batch_train_ds:\n",
        "      training_digits = training_digits.numpy()\n",
        "      training_labels = training_labels.numpy()\n",
        "      break\n",
        "    \n",
        "  else:\n",
        "    v_images, v_labels = validation_dataset.make_one_shot_iterator().get_next()\n",
        "    t_images, t_labels = batch_train_ds.make_one_shot_iterator().get_next()\n",
        "    # Run once, get one batch. Session.run returns numpy results\n",
        "    with tf.Session() as ses:\n",
        "      (validation_digits, validation_labels,\n",
        "       training_digits, training_labels) = ses.run([v_images, v_labels, t_images, t_labels])\n",
        "  \n",
        "  # these were one-hot encoded in the dataset\n",
        "  validation_labels = np.argmax(validation_labels, axis=1)\n",
        "  training_labels = np.argmax(training_labels, axis=1)\n",
        "  \n",
        "  return (training_digits, training_labels,\n",
        "          validation_digits, validation_labels)\n",
        "\n",
        "# create digits from local fonts for testing\n",
        "def create_digits_from_local_fonts(n):\n",
        "  font_labels = []\n",
        "  img = PIL.Image.new('LA', (28*n, 28), color = (0,255)) # format 'LA': black in channel 0, alpha in channel 1\n",
        "  font1 = PIL.ImageFont.truetype(os.path.join(MATPLOTLIB_FONT_DIR, 'DejaVuSansMono-Oblique.ttf'), 25)\n",
        "  font2 = PIL.ImageFont.truetype(os.path.join(MATPLOTLIB_FONT_DIR, 'STIXGeneral.ttf'), 25)\n",
        "  d = PIL.ImageDraw.Draw(img)\n",
        "  for i in range(n):\n",
        "    font_labels.append(i%10)\n",
        "    d.text((7+i*28,0 if i<10 else -4), str(i%10), fill=(255,255), font=font1 if i<10 else font2)\n",
        "  font_digits = np.array(img.getdata(), np.float32)[:,0] / 255.0 # black in channel 0, alpha in channel 1 (discarded)\n",
        "  font_digits = np.reshape(np.stack(np.split(np.reshape(font_digits, [28, 28*n]), n, axis=1), axis=0), [n, 28*28])\n",
        "  return font_digits, font_labels\n",
        "\n",
        "# utility to display a row of digits with their predictions\n",
        "def display_digits(digits, predictions, labels, title, n):\n",
        "  fig = plt.figure(figsize=(13,3))\n",
        "  digits = np.reshape(digits, [n, 28, 28])\n",
        "  digits = np.swapaxes(digits, 0, 1)\n",
        "  digits = np.reshape(digits, [28, 28*n])\n",
        "  plt.yticks([])\n",
        "  plt.xticks([28*x+14 for x in range(n)], predictions)\n",
        "  plt.grid(b=None)\n",
        "  for i,t in enumerate(plt.gca().xaxis.get_ticklabels()):\n",
        "    if predictions[i] != labels[i]: t.set_color('red') # bad predictions in red\n",
        "  plt.imshow(digits)\n",
        "  plt.grid(None)\n",
        "  plt.title(title)\n",
        "  display.display(fig)\n",
        "  \n",
        "# utility to display multiple rows of digits, sorted by unrecognized/recognized status\n",
        "def display_top_unrecognized(digits, predictions, labels, n, lines):\n",
        "  idx = np.argsort(predictions==labels) # sort order: unrecognized first\n",
        "  for i in range(lines):\n",
        "    display_digits(digits[idx][i*n:(i+1)*n], predictions[idx][i*n:(i+1)*n], labels[idx][i*n:(i+1)*n],\n",
        "                   \"{} sample validation digits out of {} with bad predictions in red and sorted first\".format(n*lines, len(digits)) if i==0 else \"\", n)\n",
        "\n",
        "def plot_learning_rate(lr_func, epochs):\n",
        "  xx = np.arange(epochs+1, dtype=np.float)\n",
        "  y = [lr_decay(x) for x in xx]\n",
        "  fig, ax = plt.subplots(figsize=(9, 6))\n",
        "  ax.set_xlabel('epochs')\n",
        "  ax.set_title('Learning rate\\ndecays from {:0.3g} to {:0.3g}'.format(y[0], y[-2]))\n",
        "  ax.minorticks_on()\n",
        "  ax.grid(True, which='major', axis='both', linestyle='-', linewidth=1)\n",
        "  ax.grid(True, which='minor', axis='both', linestyle=':', linewidth=0.5)\n",
        "  ax.step(xx,y, linewidth=3, where='post')\n",
        "  display.display(fig)\n",
        "\n",
        "class PlotTraining(tf.keras.callbacks.Callback):\n",
        "  def __init__(self, sample_rate=1, zoom=1):\n",
        "    self.sample_rate = sample_rate\n",
        "    self.step = 0\n",
        "    self.zoom = zoom\n",
        "    self.steps_per_epoch = 60000//BATCH_SIZE\n",
        "\n",
        "  def on_train_begin(self, logs={}):\n",
        "    self.batch_history = {}\n",
        "    self.batch_step = []\n",
        "    self.epoch_history = {}\n",
        "    self.epoch_step = []\n",
        "    self.fig, self.axes = plt.subplots(1, 2, figsize=(16, 7))\n",
        "    plt.ioff()\n",
        "\n",
        "  def on_batch_end(self, batch, logs={}):\n",
        "    if (batch % self.sample_rate) == 0:\n",
        "      self.batch_step.append(self.step)\n",
        "      for k,v in logs.items():\n",
        "        # do not log \"batch\" and \"size\" metrics that do not change\n",
        "        # do not log training accuracy \"acc\"\n",
        "        if k=='batch' or k=='size':# or k=='acc':\n",
        "          continue\n",
        "        self.batch_history.setdefault(k, []).append(v)\n",
        "    self.step += 1\n",
        "\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    plt.close(self.fig)\n",
        "    self.axes[0].cla()\n",
        "    self.axes[1].cla()\n",
        "      \n",
        "    self.axes[0].set_ylim(0, 1.2/self.zoom)\n",
        "    self.axes[1].set_ylim(1-1/self.zoom/2, 1+0.1/self.zoom/2)\n",
        "    \n",
        "    self.epoch_step.append(self.step)\n",
        "    for k,v in logs.items():\n",
        "      # only log validation metrics\n",
        "      if not k.startswith('val_'):\n",
        "        continue\n",
        "      self.epoch_history.setdefault(k, []).append(v)\n",
        "\n",
        "    display.clear_output(wait=True)\n",
        "    \n",
        "    for k,v in self.batch_history.items():\n",
        "      self.axes[0 if k.endswith('loss') else 1].plot(np.array(self.batch_step) / self.steps_per_epoch, v, label=k)\n",
        "      \n",
        "    for k,v in self.epoch_history.items():\n",
        "      self.axes[0 if k.endswith('loss') else 1].plot(np.array(self.epoch_step) / self.steps_per_epoch, v, label=k, linewidth=3)\n",
        "      \n",
        "    self.axes[0].legend()\n",
        "    self.axes[1].legend()\n",
        "    self.axes[0].set_xlabel('epochs')\n",
        "    self.axes[1].set_xlabel('epochs')\n",
        "    self.axes[0].minorticks_on()\n",
        "    self.axes[0].grid(True, which='major', axis='both', linestyle='-', linewidth=1)\n",
        "    self.axes[0].grid(True, which='minor', axis='both', linestyle=':', linewidth=0.5)\n",
        "    self.axes[1].minorticks_on()\n",
        "    self.axes[1].grid(True, which='major', axis='both', linestyle='-', linewidth=1)\n",
        "    self.axes[1].grid(True, which='minor', axis='both', linestyle=':', linewidth=0.5)\n",
        "    display.display(self.fig)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Lz1Zknfk4qCx"
      },
      "source": [
        "### tf.data.Dataset: parse files and prepare training and validation datasets\n",
        "Please read the [best practices for building](https://www.tensorflow.org/guide/performance/datasets) input pipelines with tf.data.Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ZE8dgyPC1_6m",
        "colab": {}
      },
      "source": [
        "AUTO = tf.data.experimental.AUTOTUNE\n",
        "\n",
        "def read_label(tf_bytestring):\n",
        "    label = tf.decode_raw(tf_bytestring, tf.uint8)\n",
        "    label = tf.reshape(label, [])\n",
        "    label = tf.one_hot(label, 10)\n",
        "    return label\n",
        "  \n",
        "def read_image(tf_bytestring):\n",
        "    image = tf.decode_raw(tf_bytestring, tf.uint8)\n",
        "    image = tf.cast(image, tf.float32)/256.0\n",
        "    image = tf.reshape(image, [28*28])\n",
        "    return image\n",
        "  \n",
        "def load_dataset(image_file, label_file):\n",
        "    imagedataset = tf.data.FixedLengthRecordDataset(image_file, 28*28, header_bytes=16)\n",
        "    imagedataset = imagedataset.map(read_image, num_parallel_calls=16)\n",
        "    labelsdataset = tf.data.FixedLengthRecordDataset(label_file, 1, header_bytes=8)\n",
        "    labelsdataset = labelsdataset.map(read_label, num_parallel_calls=16)\n",
        "    dataset = tf.data.Dataset.zip((imagedataset, labelsdataset))\n",
        "    return dataset \n",
        "  \n",
        "def get_training_dataset(image_file, label_file, batch_size):\n",
        "    dataset = load_dataset(image_file, label_file)\n",
        "    dataset = dataset.cache()  # this small dataset can be entirely cached in RAM, for TPU this is important to get good performance from such a small dataset\n",
        "    dataset = dataset.shuffle(5000, reshuffle_each_iteration=True)\n",
        "    dataset = dataset.repeat() # Mandatory for Keras for now\n",
        "    dataset = dataset.batch(batch_size, drop_remainder=True) # drop_remainder is important on TPU, batch size must be fixed\n",
        "    dataset = dataset.prefetch(AUTO)  # fetch next batches while training on the current one (-1: autotune prefetch buffer size)\n",
        "    return dataset\n",
        "  \n",
        "def get_validation_dataset(image_file, label_file):\n",
        "    dataset = load_dataset(image_file, label_file)\n",
        "    dataset = dataset.cache() # this small dataset can be entirely cached in RAM, for TPU this is important to get good performance from such a small dataset\n",
        "    dataset = dataset.batch(10000, drop_remainder=True) # 10000 items in eval dataset, all in one batch\n",
        "    dataset = dataset.repeat() # Mandatory for Keras for now\n",
        "    return dataset\n",
        "\n",
        "# instantiate the datasets\n",
        "training_dataset = get_training_dataset(training_images_file, training_labels_file, BATCH_SIZE)\n",
        "validation_dataset = get_validation_dataset(validation_images_file, validation_labels_file)\n",
        "\n",
        "# For TPU, we will need a function that returns the dataset\n",
        "training_input_fn = lambda: get_training_dataset(training_images_file, training_labels_file, BATCH_SIZE)\n",
        "validation_input_fn = lambda: get_validation_dataset(validation_images_file, validation_labels_file)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "_fXo6GuvL3EB"
      },
      "source": [
        "### Let's have a look at the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "yZ4tjPKvL2eh",
        "outputId": "05c66331-7997-4b82-b5aa-ca83ef527701",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177
        }
      },
      "source": [
        "N = 24\n",
        "(training_digits, training_labels,\n",
        " validation_digits, validation_labels) = dataset_to_numpy_util(training_dataset, validation_dataset, N)\n",
        "display_digits(training_digits, training_labels, training_labels, \"training digits and their labels\", N)\n",
        "display_digits(validation_digits[:N], validation_labels[:N], validation_labels[:N], \"validation digits and their labels\", N)\n",
        "font_digits, font_labels = create_digits_from_local_fonts(N)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KS0tFkVWVnTt3kqGhIXEcR927dxctolpZWRm/WnDp0iWF72bOnMm7flR9xgcP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\nDEYj5P8DRV5HECRgVRUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/RKFeUFBATk5O4jX08PCgKVOm0JYtW2jLli3k4eEhru/iOE5SD7uQsvjgwYPN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\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "KIc0oqiD40HC"
      },
      "source": [
        "### Keras model\n",
        "If you are not sure what cross-entropy, dropout, softmax or batch-normalization mean, head here for a crash-course: [Tensorflow and deep learning without a PhD](https://github.com/GoogleCloudPlatform/tensorflow-without-a-phd/#featured-code-sample)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "56y8UNFQIVwj",
        "outputId": "fdb49885-bb2c-4cac-bfe6-d182fa28a448",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        }
      },
      "source": [
        "model = tf.keras.Sequential(\n",
        "  [\n",
        "      tf.keras.layers.Reshape(input_shape=(28*28,), target_shape=(28, 28, 1)),\n",
        "      \n",
        "      tf.keras.layers.Conv2D(kernel_size=3, filters=12, use_bias=False, padding='same'),\n",
        "      tf.keras.layers.BatchNormalization(center=True, scale=False),\n",
        "      tf.keras.layers.Activation('relu'),\n",
        "      \n",
        "      tf.keras.layers.Conv2D(kernel_size=6, filters=24, use_bias=False, padding='same', strides=2),\n",
        "      tf.keras.layers.BatchNormalization(center=True, scale=False),\n",
        "      tf.keras.layers.Activation('relu'),\n",
        "      \n",
        "      tf.keras.layers.Conv2D(kernel_size=6, filters=32, use_bias=False, padding='same', strides=2),\n",
        "      tf.keras.layers.BatchNormalization(center=True, scale=False),\n",
        "      tf.keras.layers.Activation('relu'),\n",
        "      \n",
        "      tf.keras.layers.Flatten(),\n",
        "      \n",
        "      tf.keras.layers.Dense(200, use_bias=False),\n",
        "      tf.keras.layers.BatchNormalization(center=True, scale=False),\n",
        "      tf.keras.layers.Activation('relu'),\n",
        "      \n",
        "      tf.keras.layers.Dropout(0.3),\n",
        "      tf.keras.layers.Dense(10, activation='softmax')\n",
        "  ])\n",
        "\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.01),\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# print model layers\n",
        "model.summary()\n",
        "\n",
        "# utility callback that displays training curves\n",
        "plot_training = PlotTraining(sample_rate=10, zoom=16)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "reshape_5 (Reshape)          (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_15 (Conv2D)           (None, 28, 28, 12)        108       \n",
            "_________________________________________________________________\n",
            "batch_normalization_20 (Batc (None, 28, 28, 12)        36        \n",
            "_________________________________________________________________\n",
            "activation_20 (Activation)   (None, 28, 28, 12)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_16 (Conv2D)           (None, 14, 14, 24)        10368     \n",
            "_________________________________________________________________\n",
            "batch_normalization_21 (Batc (None, 14, 14, 24)        72        \n",
            "_________________________________________________________________\n",
            "activation_21 (Activation)   (None, 14, 14, 24)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_17 (Conv2D)           (None, 7, 7, 32)          27648     \n",
            "_________________________________________________________________\n",
            "batch_normalization_22 (Batc (None, 7, 7, 32)          96        \n",
            "_________________________________________________________________\n",
            "activation_22 (Activation)   (None, 7, 7, 32)          0         \n",
            "_________________________________________________________________\n",
            "flatten_5 (Flatten)          (None, 1568)              0         \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 200)               313600    \n",
            "_________________________________________________________________\n",
            "batch_normalization_23 (Batc (None, 200)               600       \n",
            "_________________________________________________________________\n",
            "activation_23 (Activation)   (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 10)                2010      \n",
            "=================================================================\n",
            "Total params: 354,538\n",
            "Trainable params: 354,002\n",
            "Non-trainable params: 536\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E0A7CSbW67EY",
        "colab_type": "text"
      },
      "source": [
        "### Learning Rate schedule"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2qz_k44U6_-f",
        "colab_type": "code",
        "outputId": "7c7ff914-6cf0-473a-9b77-77df977baa47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "source": [
        "# lr decay function\n",
        "def lr_decay(epoch):\n",
        "  return 0.01 * math.pow(0.666, epoch)\n",
        "\n",
        "# lr schedule callback\n",
        "lr_decay_callback = tf.keras.callbacks.LearningRateScheduler(lr_decay, verbose=True)\n",
        "\n",
        "# important to see what you are doing\n",
        "plot_learning_rate(lr_decay, EPOCHS)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py:514: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
            "  max_open_warning, RuntimeWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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15+0eT9fHbg/Chw71+LOcXm+3S0fRQADOrKzux2Z5EQ0Ejjo22OOxqRD64ssu5y2zIB9q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\nIiKSEocYIiIiktJ/AaNkkWPaV24tAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "CuhDh8ao8VyB"
      },
      "source": [
        "### Train and validate the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "TTwH_P-ZJ_xx",
        "outputId": "23d7b887-192b-4802-f558-60053193a35b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "source": [
        "steps_per_epoch = 60000//BATCH_SIZE  # 60,000 items in this dataset\n",
        "print(\"Steps per epoch: \", steps_per_epoch)\n",
        "\n",
        "history = model.fit(training_dataset, steps_per_epoch=steps_per_epoch, epochs=EPOCHS,\n",
        "                    validation_data=validation_dataset, validation_steps=1, callbacks=[plot_training, lr_decay_callback])"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Srp1e9eVr9LfTklSwfR9cuQqTL7wAjpHVgx2tLQCAwZWrMOOrVwMAGJatSvus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QtbGK3O54GMrUG9pw7uRpvLwQW5avd4DlRDcerCclDmwHIu1YsZ3ytP8ulsou+DA+IxfnWcAd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\nd7Vxv5EHtu9ht9vhcrngcrkkdTHEy16vt+h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lLfZBR+vp+a+VG9DVU9Cl+AOFcgix8vHE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\nIAiC6BOQAUsQBEEQBEEQBEH0Cf4/dNE+TwnHgR0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r937/937 [==============================] - 10s 10ms/step - loss: 0.0027 - acc: 0.9993 - val_loss: 0.0171 - val_acc: 0.9949\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9jFVovcUUVs1"
      },
      "source": [
        "### Visualize predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "w12OId8Mz7dF",
        "outputId": "5be2f10d-42ad-4ea3-95c7-10fb240800f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        }
      },
      "source": [
        "# recognize digits from local fonts\n",
        "probabilities = model.predict(font_digits, steps=1)\n",
        "predicted_labels = np.argmax(probabilities, axis=1)\n",
        "display_digits(font_digits, predicted_labels, font_labels, \"predictions from local fonts (bad predictions in red)\", N)\n",
        "\n",
        "# recognize validation digits\n",
        "probabilities = model.predict(validation_digits, steps=1)\n",
        "predicted_labels = np.argmax(probabilities, axis=1)\n",
        "display_top_unrecognized(validation_digits, predicted_labels, validation_labels, N, 7)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py:514: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
            "  max_open_warning, RuntimeWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lqta+wtQXL++zw0eQ/fBvNOzeDSqlEqrCQsgtLCAzMEDKmbN4fu6C8H827/bD0z1/IPv+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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lS5cIAFlaWlJcXJzC5VVGSUkJjR07lg4dOkSHDh2qU1tUV44cOUJ+fn50+fLl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\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EQqlUwsHBQfDrpqWl8ZsMclzDTuh379595t8xQ72NpKen843L2dlZtE2AdKPI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\ng8HogDBDncFgMBgMBoPB6ID8fw+fezrvRx4RAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RIcSu16SsrAwuLi5Cznyxs1U8ffoUn376KRISElBUVIQHDx4ItjSvR+/evUXN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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bjFVevnw5li9fzleKEXtF2oZwMc9E1OrvXrlyBbq6unBxccG6dev48+fw4cMw\nMzPjtxsYGIg+ffrw90shbIz09HQEBgYqrBJlbGwsqJEOACdOnABRfWW1xot1NWyWlpaYNm0aduzY\nIfg6ECEhISCqX6dkz549vIHu7++PO3fuKAwN3LdvH7S1tUUpt8tVeOGqvAi9Eum9e/ewZs0aRERE\nYNCgQXKNy43jrvNu3bph4cKFguoD9bkg+/btQ3R0NGxsbHh7jjvXDAwMMHnyZERERCA3N7fFYhrt\nylCvqqrC8OHDIZFIMHfuXFFXu8vJyVG4wqCnp6egHk6g/sawZcsWpKWloaqqSunjsre3h0QiaeJJ\nU0RNTY1cuckHDx7g999/55+Q30RdXR1u3LjBD85Ewq+4l52djffffx9EhHXr1gm6bQD4448/4Onp\n2ezshJWVFaysrDRirL98+ZLPE9DS0sKsWbMEq4/MJUI3PFZdXV25KhVCUFZWxlctICKcPXsW/v7+\n0NfXVyrpav369XBxcVFa7/z58zh//jzGjh3bYlnWR48eIT4+Xq5cI9eEOM8aGuqDBg1q8bP379+X\nq/RiaGiIgwcPtkm3oaGemZmJzMxM/PHHH3B0dOQHmilTpii87166dKnNhjoXl21hYQETExMUFBRg\nypQpkEjqFxuaOXNms3W0ufwIIZOGOW7evMkb6FysupB5FzKZTK4Slo6ODkaNGoVRo0Zh3759fCUY\nIoK1tbXKeTzcrI+iNnPmTKUq+6xcuRIrV67kvydksvibkMlkvNe1LSQmJsLBwaHZmV8rKyuMGTMG\nERERgiWTZmRkwMTERGGf+/v7C6LRmIqKCvz000+YM2cOoqOj5drFixdhb28PExMTwQoqNKa0tJQv\n62pvbw8dHR14eXnh9OnTLVY5O3z4MKRSqeCGesMyjET1ddbVycKFC0FUnx9XXFyM8vJywbZ98uRJ\nDB06FEOHDpWrHvSmMsLcmhzNzRi1G0OdM9I5A1GIJM7mkMlkTcJdLCwsYGFhgdTUVNF0W0NsbCxf\naUIZQ33p0qXo2rVrmweun376ib9wuBVhhfQslJeXY+TIkSAijB8/XrDk0YZwKxJKJPWVOU6ePAlv\nb+8mBvvevXsF126J4uJizJo1izfS58yZI9i2c3Nz0atXL75iAfdAkpycLJhGQ7jlrImIL834Jm9I\nfn4+8vPz0atXr1YlkzZMKFMU/nbhwgVs3boV48ePV3gDnDBhQquPr7n9b3ifaM5Df/78ef7hurWJ\np4q4cuUKX51p3bp1WLduHY4fP85fpy0twJabm6uyR33IkCHo1KkTv3Kxs7Nzs6VquTrFFhYW8PPz\na5NeS5w8eRKdO3eWSyYVumxaP78iAAAVcElEQVRuYWEhjI2NERQUhNjY2Cbe6TNnzvCGhaqVZYD6\n8BFFg7afn5/S98dJkyZh0qRJ/GAvxH4pS1ZWVps96hw1NTWQyWQ4fvw4v/psbGzsGysqtYX09PQm\nRnq3bt344glCO+iUgQtJEbLqV2Oys7P5evhE9aUZ33SeHDp0CEZGRujZs6egY3VeXp5cGUZ1FRdo\niK2tLaysrAQvxTx8+HC5hYwUteYMda5xs5eNaTeGemRkJD/4i1kuJzMzkx/YG4a6vKkmsjqJjY3l\na8fb2dkpFe9pZWWFTp06tfrp98GDB/Dx8eHDMfr27YtTp07h1KlTgl2g5eXlfLiEi4uLIAvtKOKr\nr76S+02B+nANX19fud977ty5oug3pKqqClVVVbh58yYfhtSpUyeF9ZHbyv379+Hh4SF3bMbGxqKu\ncsqFo3FhF87OzgpveDKZDM+ePcO2bdtgYmICExMTWFpatuoaCwgIQEBAgNzx9erVS2FOCedh5sIH\nhKzs07jqi6LVEb///nu56VSJRAJ/f3+VZ2+4ykA9evRAjx498PHHH/N/K5pleP36NU6ePMlXeFDF\nUG8Y2mBlZdUkZOnFixdISEhASEgIf8y2traC13i/efMmH3dva2vLhwiqE865Q0SChey9ePECLi4u\nTQbr0NDQFksC19XV4e7du5g0aRK0tLSgpaUFbW1trF69WpD9UoZnz57xISnvvfeeyts7cOAAiAhr\n164VZRa9vLxcLtxFV1cXYWFhooVoKUN+fj7s7Ozg4eEh6uJl9+/fl6vq0ni109u3b+PYsWOYNWsW\n77Dk7rVCz4xxuXOaMNCB+pKn2traKjlQmsPGxqZFI5wz1N3d3eHh4SFn1HOVAJtbQE3jhjo3sOjr\n68PU1FSUBEaOkpISuLm5yQ2mc+bM0ciTtCLKysqwadMmPqadW4RHGS5cuAAjIyOYmZlhxIgRWL9+\nPZKTk/H8+XO+5ebmYv369Xzz8fHh4y+NjY3x9ddfK6zvqirLli3jDQYxF49qaKiXlZXh3LlzCA4O\nbpIj8KZYalXJzc3FhAkT5BJaBgwYoLAslyr07t27icHar18/QTUaw51LXMIdl5Azbdo0REVFYc2a\nNRg5ciQ/MGhpafGLELU270Mmk0EmkyEoKKhFr4SDgwNmzJiBM2fO8A9IQvLixQvY2Njwfezu7o5X\nr17xq7suX74cWlpacoa6v7+/IGEZ3KxF46anp4clS5bwnvbFixeje/fufFgS9xk9Pb02L0PeMHyG\niPiHoKlTp8LV1VUuKTwiIgIREREqrRytCC4mnYtHb7jSsDrhvO09evSQW6FUVU6dOqVwQNfV1cXU\nqVORlJSEa9euYcqUKZg6dSqmTp2KkJAQ/nNciEhLi/gJzfHjx/l1EnR0dFTOI3v+/DlMTEwQGBgo\naBhCQ9LS0uT6V+iY6NZSV1eHzz//HB07dhQsH64xNTU1qKmpaVInn8u14VrDNVXMzMxgZmaGwMDA\nZosCtBUuR0moWultISIiAkSk0NmiKsoY6keOHOFnik6dOoWEhAQkJCTws87NoXFDPSYmRi0hCRUV\nFdi6datcBRYvLy9RK8ooS2lpKdLT0+Hn5yfnGW1tFvTatWvh5uamcGBvrllbW+P777/HjRs3RDm2\nK1euQEdHBwYGBqJO7wHgV3jljCkzMzO5/lyyZAmWLFkial3YCxcu8FO4nCdywYIFgj8MJicnw8DA\noEl+hVizFRyccdqxY0cMGjQIY8aMkTMOieoThQcMGIAxY8YI4n2sqKhAVFQUnzi3bNky/v+///67\nWh60o6Ki5PrayclJLqmxoVff1tZWsHUfXr16hYiICLnEqzc1Z2dnzJkzB3/99ZdKyYXjxo2DoaEh\nli1bhuDgYHh6esLExAR6enrw9PTE8OHD8csvvwhunHM0jEnv16+foPHorWX16tUgIixatEjQ7f75\n559y9dBbM2U+ePBgvmKLmDx8+BDLli3DyJEjMXLkSN5Id3Z2FiQPZuLEiTAwMFC6DnxruXPnjlx1\nl4iICI0tcsdx7do1EBEWL14smgZX5YyIMGXKFMhkMuzZswdBQUF8wQVfX18EBQVh2bJlOHjwIEpL\nS0W5n3LVXWxsbFRef0AVRo8eDYlE0ua1JVqiJUO9c+fOzYa1KIMytrekuLgYpCSdOnVS9qP0/Plz\n6tmzJz19+pSWLl1Ky5YtU/q7rWXZsmUUGRnJ/+3o6EiZmZn0zjvviKbZmEePHlHXrl3lXrtw4QJ9\n/vnndPfuXf41X19f2rVrF1lbW7da4/r165Senk6XLl2iGzduKPyMlZUVDRs2jDp06ECff/456ejo\ntFpHGe7evUtDhw6l3NxcioqKogULFoiiwzF9+nTasWNHk9cNDAwoKSmJBg4cKJp2TU0NJSQk0MSJ\nE6miooJsbGyIiOjYsWPUu3dvQbXS0tJo7Nix9PTpUyIi+sc//kFERKdOnSJbW1tBtRrz6NEjIiIa\nMGAAZWZm0rvvvkvl5eX0+vVr/jMdOnQgY2NjUfdD3Tx9+pRmzJhBiYmJzX4GAHl4eNA333xDI0eO\nFFQ/JyeHiIh8fHzoyZMnNHnyZLn3jY2Nafz48UREZGdnR0ZGRiprfv/993Tw4EG6fPky/1pRURHV\n1NSQubm5yttvifv379OAAQOooKCA3NzcKDk5md59911RNVvC0NCQDA0NKScnhwwMDATddu/even6\n9eu0adMmIiJycHCggoIC2rBhA12/fp2I6s8tiURCISEhZGNjQ//1X/9FXbp0IV1d3VZpcWNsx44d\niYjo008/Vfi506dPU35+PmVmZlJGRga9fPmSf69Pnz40efJk+uyzz8jU1LS1hyvHb7/9RkOHDqUd\nO3ZQSEiISttqju3bt9Ps2bOJiMjCwoKuXLlCVlZWomgpi7u7O1VXV9Pvv/9Oenp6Gt0XsXn48CF9\n/PHH9PDhQ4qLi6OxY8eqfR+48cnDw4PeeecdOn/+vOAa3bp1o8ePH8u91qFDB5o0aRLNmTOHevbs\n2eZtv3r16s0fEsOjLpPJ+FJeU6ZMafOThjJUVlbCy8tLziOmo6MDKysrjBgxQlRtoL6u8pQpUzBs\n2DCsW7cO7u7ucHd3h729PR+Woa2tDW9vb/z6668af9oXgsrKSowaNQpE9bWd1XFMkZGRcgmVvXr1\nwooVK0Sv8pKXl8eHKEilUsyYMQNlZWWiJUQfOXJE7lwOCwvT+FTu34GqqiosXLiw2apCYifBq5vU\n1FS89957giddKaPLxaQ7OjqKVg1DGbjQIh0dnWbjR/+doFbMtnLNwMCAX9H5yJEjLcbPt5ZNmzbB\n1dVV1HMsKyuL93aK6cFWBs5jbWVl1eaQtH8n8vLy+NWVNTlGcXktYlWkAoDo6GhYWVnxXnRbW1vs\n2LFDkG0rY3uzlUkZDAaDwWAwGIz2iBge9VWrVkEiqV84QqwEEqDes2tiYtLE+2Vubo6oqCjR4isb\n8ujRo2a9cBKJBO+9956gq3K1B6ZPn85XeREzo70hr1+/RmpqKlJTU0WtHNSQ8+fP8zHpVlZWoibL\ncnAe9Z49eyIxMZFPumSIT01NDebPny93/bq7u2Px4sWorKzU9O7925OVlQVLS0s+eVTIxYzaAldH\n3djYWK2lD8Xi22+/RUhICN8alu1r2Ly9vRESEoKEhIR2Uw2N8e8Hl0CqqSovHAcOHMCBAwcgkUha\nXFiovaKRGPW8vDzy8PCgoqIiWr58uaixyzU1NTRw4EC6cOEC/5qJiQmlpKSQm5ubaLoNKS4upj59\n+lBubi4RER+rFBgYSKNHj6YuXbqQmZmZWvZFHZw4cYL8/f1JR0eHEhMTydfXV9O7JAoXL16kjz76\niGpqamj48OG0efNmPl6cwWC0jsrKSurduzdlZ2eTm5sbpaSkkImJiaZ3i8FgtAEuNp2IKD09nc/b\nYrQeZWLUtYQULC4uJnd3d3r58iXt3r1btAQSDi0tLVq8eDGfNBMSEkK7du0ibW1tUXUbYmxsTA8e\nPFCbnqY4c+YMERFNmjSJ6urqKCIi4q010omI+vXrR9XV1ZreDQbjreDs2bOUnZ1N77zzDu3evZsZ\n6QzGvzEbN26khw8fUlhYGDPS1YAgHnXuicDV1ZVyc3NpwoQJtH//fmH2kMFgMBgMBoPBeMtQxqPe\nKkO9Y8eOVFdXp9JOMRgMBoPBYDAYf2ekUimVlpa+8XOtCn1RZoMMBoPBYDAYDAZDdVh5RgaDwWAw\nGAwGox3CDHUGg8FgMBgMBqMdwgx1BoPBYDAYDAajHcIMdQaDwWAwGAwGox3CDHUGg8FgMBgMBqMd\nwgx1BoPBYDAYDAajHSK6oW5tbS3XTE1Naf78+WLLakyXiGjatGnk6OhINjY29OGHH1JMTMxbq6vJ\nfs7NzaWgoCCytbUlBwcHmj9/PtXU1Iiuq6lj/vHHH2nIkCFkYWFBM2fOFF2vMfHx8eTu7k5dunQh\nV1dXysjIUIvu8OHDydLSku9vNzc3tegWFRXRhAkTqEuXLuTi4kKHDx9Wiy6R5vpaE7p37tyhkSNH\nUrdu3ahPnz507Ngx0TUbkpOTQ5aWljRt2jS16mqirzU1NnGou681fc/8u5xbmrQDONTd15WVlTR7\n9mxycXGhrl270oABA+jkyZOiaLWqjnpbePz4Mf9/mUxGjo6OFBAQILasxnSJiMLCwig6Opp0dXUp\nOzubRowYQb169SJXV9e3TleT/Txv3jwyMzOjO3fu0KtXrygwMJB27txJM2bMEFVXU8dsZWVF8+bN\no19//ZXKy8tF12vI6dOnKSIigvbs2UMffvghFRQUqFV/7dq19Pnnn6tVc968eaSjo0PZ2dmUlZVF\n48aNIxcXF3J2dhZVV1N9rQndmpoa+uyzzyg0NJQSEhLot99+o+DgYHJ2dqbu3buLrk9U/zv37dtX\nLVocmvqNNTU2cai7rzV5zyT6+5xbmrQDONTd1zU1NWRtbU3Hjx8nGxsbOnHiBIWGhlJ6ejrZ2toK\nqqXW0JejR4+SmZkZffTRR+qUVbuus7Mz6erqEhGRRCIhiURCDx48eGt1OdTdz7m5uRQYGEh6enpk\naWlJn3zyCd2+fVst2hzqPOZPP/2URowYQaampqJrNWb16tW0YMEC6tevH0mlUurSpQt16dJF7fuh\nLsrKyujo0aO0ePFiMjQ0JE9PT/Lz86O4uDjRtTXV15rQzc7OpoKCApo1axZ16NCBBg8eTP3796eD\nBw+KqssRHx9PnTp1okGDBqlFj0NTv7EmxwhN9LUm75l/t3OLQxN2nib62sDAgMLDw8nW1pakUin5\n+flRt27d6OrVq4JrqdVQj42NpfHjx5NEIlGnrEZ0/+d//oc6d+5M/fr1I0tLSxo6dOhbrUuk/n6e\nOXMmxcfH0+vXryk/P59OnTpFn3zyiVq0OTR1TquT2tpaunLlCr148YL69OlDH3zwAc2fP1+tHqrl\ny5fT+++/T8OGDaO0tDTR9e7du0daWlpyXt2ePXvSrVu3RNXVVF+3h9+YA4Do/UxEVFJSQqtWraKV\nK1eKrtUQTfe1JsYITfW1pvi7nltE6h8T28u5VVhYSDk5OaLMuKrNUM/Ly6P09HQKDg5Wl6RGddev\nX0+PHj2i5ORkGjlyJO/FeFt1NdHPH330Ed2+fZtsbGzogw8+IFdXVxoxYoTa9DV1bqmbwsJCqq6u\npsTEREpOTqa0tDS6du0arVu3Ti36y5cvp6tXr9KtW7foiy++oODgYNG9gGVlZdSxY0e514yMjEgm\nk4mqq6m+1pSuvb09mZmZ0ZYtW6i6upp+/fVXSk9PV4thsXLlSpo4cSJZW1uLrtUQTV9PmhgjNNXX\nmuLvem5pYkxsD+dWdXU1TZ06lYKDg8nBwUHw7avNUI+LiyMPDw+ys7NTl6RGdYmIOnToQJ6enpSf\nn0+7du16q3XV3c91dXX0n//5nzRy5EjKz8+n+/fvU3FxMUVERKhFn0iz55Y60dfXJ6L6RDQrKyt6\n99136csvv6QTJ06oRd/NzY06duxIurq69Nlnn1H//v1F1zYwMKDS0lK510pKSsjQ0FBUXU31taZ0\ntbW16cCBA5SSkkIODg70v//7vxQYGCj6VP21a9fo7Nmz9OWXX4qqowhNX09E6h0jNNnXmuDvfG6p\ne0xsD+dWXV0dTZ8+nXR0dGjt2rWiaIieTMpx8OBBmjt3rrrkNK7bkJqaGrXGimtCV939XFRURI8e\nPaKpU6eSrq4u6erq0oQJE2jlypUUGRmpln1oD+eWOjA2NiZra2u5qUxNhvpIJBICIKpG9+7dqaam\nhnJycugf//gHERFdv35d9ERSTfW1Jn9jFxcXSkpK4v/29fUV3SP322+/UV5eHrm4uBBR/QxKbW0t\n3b59m86dOyeqdnu6ntQxRmiyrzXB3/ncUveYqOlzCwDNnj2bCgsL6fDhw6StrS2Kjlo86hcuXKAn\nT56oPQtYE7rPnj2j+Ph4kslkVFtbS6mpqRQfH0+DBw9+K3WJNNPP7777Ltna2tLu3buppqaGiouL\nKTY2lnr06KEWfU0cc01NDVVUVFBtbS3V1tZSRUWFWspREhF99tln9OOPP9KzZ8+ouLiYtm/fTsOG\nDRNdt7i4mFJTU/ljPXToEGVkZJCPj4+ougYGBjRy5EhatWoVlZWV0fnz5yk5OZnGjRsnqi6R5vpa\nU7rXr1+niooKev36NUVHR1NBQQF99tlnompOmjSJrly5QmlpaZSWlkahoaHk6+tLP//8s6i6HJro\na02NEZrsa03cM/+O5xaRZsZETff1119/TdnZ2XTw4EF+NkMM1OJRj42NpREjRjSJ+XwbdSUSCe3a\ntYvCwsIIANnY2NDq1avpP/7jP95KXSLN/b779++n8PBw2rRpE3Xo0IEGDRpEq1atUou2Jo557dq1\nFBUVxf996NAhWrhwIYWHh4uuvWDBAnr58iV9+OGHpKenRwEBATRv3jzRdWtqaui7776ju3fvklQq\nJQcHBzpw4IBaSvetX7+eZs2aRfb29mRqakrr168X3aNOpLm+1pRuXFwcxcTEUE1NDXl6elJCQoLo\ncdPvvPMOvfPOO/zfBgYGpKenR2ZmZqLqcmiirzU1RmiyrzVxz/w7nltEmhkTNdnXeXl5tGfPHtLV\n1SVHR0f+9Y0bN9LYsWMF1ZIUFxeLO4fMYDAYDAaDwWAwWo1ayzMyGAwGg8FgMBgM5WCGOoPBYDAY\nDAaD0Q5hhjqDwWAwGAwGg9EOYYY6g8FgMBgMBoPRDmGGOoPBYDAYDAaD0Q5hhjqDwWAwGAwGg9EO\nYYY6g8FgMBgMBoPRDmGGOoPBYDAYDAaD0Q5hhjqDwWAwGAwGg9EO+X9Vdl9dqCYDBAAAAABJRU5E\nrkJggg==\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WLl1ap0xQXbp0ERbiV7c+RUxGjhyJjh07SrYo+n3wGWJ69uwJU1NTSQZqiYmJ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\nqDMYDAaDwWAwGI2Q/wcVo2nMq7kipQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7LS2NAgMDSSqVkrGxMWlra9OZM2fe2vYMGzaMey4NjYkdFhbGhXBTbMj26i7C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\nYDAYDAaDwWiEMEOdwWAwGAwGg8FohPwPLNnWi3uAyxoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8+fNwcnKCvb09jI2NER4erpY9qqioCAsWLICrq6sov/2CBQvQu3dvNaH+5MkT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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "SVY1pBg5ydH-"
      },
      "source": [
        "## License"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hleIN5-pcr0N"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "author: Martin Gorner<br>\n",
        "twitter: @martin_gorner\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Copyright 2019 Google LLC\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "\n",
        "    http://www.apache.org/licenses/LICENSE-2.0\n",
        "\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "This is not an official Google product but sample code provided for an educational purpose\n"
      ]
    }
  ]
}